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Data & AI Products Manager
Locations: Irving TX, Reading PA, Dearborn MI, Duluth GA, or Beachwood OH (possible remote for the right candidate)
Shift: Monday - Friday 8am -5pm
Position Summary:
Leads the strategy, roadmap, adoption, and value realization of Data and AI products from validated opportunity through production and continuous improvement. The Product Manager owns the product roadmap and serves as the primary business liaison, partnering with PQS, Operations, and business stakeholders to understand operational needs, define and prioritize business problems, and translate them into product direction and measurable outcomes. The Product Manager owns user adoption and business value realization, including engagement, communication, training, feedback, outcome measurement, and demonstrating product impact. Partners with Data Engineering, Data Science, Cloud Engineering, Security, IT, and other technical teams on feasibility, delivery planning, and technical execution to help products successfully scale from prototype to production, while technical teams remain accountable for how solutions are engineered and delivered
Major Responsibilities:
- Product Strategy & Roadmap – Owns the product vision, strategy, roadmap, priorities, requirements, and lifecycle for assigned Data and AI products, guiding products from validated opportunity and prototype through production and continuous improvement.
- Business & Customer Discovery – Serves as the primary business liaison and partners with PQS, Operations, and business stakeholders to understand customer and operational needs, define problems, validate opportunities, and establish product priorities and expected outcomes.
- Product Delivery & Scaling – Partners with Data Engineering, Data Science, Cloud Engineering, Security, IT, and other technical teams to assess feasibility, plan delivery, resolve dependencies, and transition products from prototype to scalable production solutions; technical teams remain accountable for technical execution.
- Adoption & Customer Engagement – Owns product adoption and user engagement, including stakeholder communication, training, change management, customer feedback, and ongoing engagement to ensure products are successfully integrated into business workflows.
- Value Realization & Product Performance – Owns the definition, measurement, and capture of expected business value, including time savings, cost savings, revenue impact, operational improvements, and other outcomes; uses performance and customer insights to demonstrate impact and guide roadmap decisions.
- Product Management Operating Model – Applies the established product management standards, processes, tools, and best practices to support the organization's transition from a project-based to product-oriented operating model.
- Other projects and tasks as assigned by manager.
Qualifications:
- Bachelor’s degree in Business, Computer Science, Engineering, Operations, Supply Chain, Logistics, Information Systems, or related field required.
- Master’s degree preferred.
- Minimum of 4 years of product management, technology, analytics, data, or related functional experience required.
- Minimum of 1 year of experience managing Data, AI, analytics, or software products preferred.
- Advanced product management skills required.
- Advanced business & analytical capabilities required.
- Advanced execution & operating skills required.
- Intermediate technical & data fluency skills required.
- Intermediate AI product capabilities required.
- Stakeholder & leadership, teamwork, organization, and communication skills required.
- Regular, predictable, full attendance is an essential function of the job.
- Willingness to travel as necessary, work the required schedule, work at the specific location required, complete Penske employment application, submit to a background investigation (to include past employment, education, and criminal history) and drug screening are required.
Physical Requirements:
- The physical and mental demands described here are representative of those that must be met by an associate to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- The associate will be required to: read; communicate verbally and/or in written form; remember and analyze certain information; and remember and understand certain instructions or guidelines.
- While performing the duties of this job, the associate may be required to stand, walk, and sit. The associate is frequently required to use hands to touch, handle, and feel, and to reach with hands and arms. The associate must be able to occasionally lift and/or move up to 25lbs/12kg.
- Specific vision abilities required by this job include close vision, distance vision, peripheral vision, depth perception and the ability to adjust focus.
Penske is an Equal Opportunity Employer
About Penske Logistics
Penske Logistics engineers state-of-the-art transportation, warehousing and freight management solutions that deliver powerful business results for market-leading companies. With operations in North America, South America, Europe and Asia, Penske and its associates help businesses move forward by increasing visibility and driving down supply-chain costs. Visit Penske Logistics to learn more.
Job Category: Information Technology
Job Family: Information Technology
Address: 3000 Auburn Dr
Primary Location: US-OH-Beachwood
Employer: Penske Logistics LLC
Req ID: 2612111
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Penske Truck Leasing, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Penske Truck Leasing AI Hiring
Penske Truck Leasing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Beachwood, OH, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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